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This paper examined the nexus between health care expenditure and economic growth in Sub-Saharan Africa over the period 1995-2014. We use the system General Method of Moments (GMM) technique to estimate the results. The findings reveal the existence of a positive and a statistically significant relationship between the two variables, precisely; health expenditure has a significant impact on the economic growth of the region. Regarding the control variables, while the effect of official development assistance on economic growth is insignificant, foreign direct investment, the active population and gross domestic savings appear as key determinants of economic growth in the region. Additionally, the results show that health care is a necessity rather than a luxury in Sub-Saharan Africa. It is therefore necessary to develop effective and efficient health care programs, increase health expenditure, make an effective use of the young population and create better environment for foreign direct investment in order to foster economic growth in Sub-Saharan Africa.

Economic growth and health expenditure in the Sub-Saharan Africa vary substantially over time and across countries. Many studies in the health economics literature identify health care expenditure as an important factor explaining differences in the level of economic growth that is economic development is to some extent attributed to better health outcomes which are partly a result of health expenditure. Adequate and efficient health related spending is widely considered as inevitable in the improvement of health status [

Moreover, most of Sub-Saharan African countries still lag behind in terms of health care expenditure and outcomes due to a variety of reasons such as low household incomes, governments’ allocations of insufficient shares of budgets to the health sector, mismanagement of resources allocated to the health sector, poor health care systems, among other things.

Previous theoretical and empirical works have shown the existence of a relationship between health expenditure and economic growth using various methodological approaches; see, inter alia, [

This study uses annual data on 36 (N = 36) Sub-Saharan African countries from 1995 to 2014 (T = 20) (see

This paper employs the two-step system Generalized Method of Moments (GMM) and the simple panel data models (fixed effect (FE) and random effect (RE)) to estimate the results. These two methods are used in order to compare the results as the GMM considers endogeneity while the simple panel data models do not.

The two-step system Generalized Method of Moments (GMM) [

To build the models, we consider the following aggregate production function adapted from the work of [

Y = A K α L β (1)

where Y is GDP per capita growth rate (GDPPCGR); A is total factor productivity, K is composite capital stock given as K = gof, where g is gross domestic savings as percentage of GDP (GDS), o is official development assistance (ODA) and f is foreign direct investment (FDI); and L is the labor composite determined by L = hp, where h is human capital proxied by health expenditure per capita (HEPC) and p is the labor force proxied by the share of population with age between 15 and 64 years (POP). By applying log to (1), we can get the following form:

ln GDPPCGR = θ + α 1 ln GDS + α 2 ln ODA + α 3 ln FDI + β 1 ln HEPC + β 2 ln POP + μ t (2)

Equation (2) can be rewritten for country i at time t to get the simple panel model:

ln GDPPCGR i t = θ i + α 1 i ln GDS i t + α 2 i ln ODA i t + α 3 i ln FDI i t + β 1 i ln HEPC i t + β 2 i ln POP i t + μ i t (3)

And following [

ln ( Y ) i t = ∑ f = 1 h β 1 ln ( Y ) i t − f + γ l ln ( X ) i t − l + δ i + ε i t (4)

E [ δ i ] = E [ ε i t ] = E [ δ i ε i t ] = 0 (5)

where Y stands for the dependent variable which is GDPPCGR; X is for the vector of the main independent variable HEPC and control variables (ODA, FDI, POP and GDS); δ i are the unobserved time-invariant country-specific effects while ε i t is the observations error term.

In line with economic theory, good health can contribute to economic growth in many ways. For instance, health expenditure is expected to improve the health of the labor force and consequently increase their productivity. An increase in labor productivity will inevitably increase gross domestic output, hence contributes to economic growth. Healthier workers with higher productivity earn higher wages [

Other control variables used are population, saving, foreign direct investment and foreign aid. Population variable is used because of the great importance of age structure in determining the level of economic growth of a country. In between 15 and 64 years age structure is the physically active population who affects more productivity vis-a-vis growth. Gross domestic savings as percentage of GDP is the investment variable. It is included based on its importance in determining the aggregate income of a country referring to economic theory.

Theoretically, investment can contribute to economic growth by generating technological diffusion through foreign direct investment (see [

An inflow of foreign capital in form of aid could affect economic growth through the provision of the foreign exchange.

The estimations are done with the help of the statistical software STATA (version 14).

Variable | Mean | Std. Dev. | Min. | Max. | Obs. |
---|---|---|---|---|---|

GDPPCGR | 2.630 | 7.805 | −37.925 | 141.641 | 720 |

HEPC | 189.698 | 234.598 | 5.943 | 1768.676 | 720 |

FDI | 4.734 | 9.749 | −5.977 | 161.823 | 720 |

ODA | 6.28E+08 | 8.6E+08 | −2E+07 | 1.27E+10 | 720 |

POP | 54.095 | 4.626 | 47.018 | 71.024 | 720 |

GDS | 13.456 | 17.326 | −125.681 | 83.287 | 720 |

Variables | Coefficients | ||
---|---|---|---|

GMM | Fixed Effect(FE) | Random Effect (RE) | |

lnGDPPCGR(−1) | 0.417 | ||

lnHEPC | 0.157** | 0.235* | 0.252** |

lnFDI | 0.209*** | 0.347** | 0.381** |

lnODA | -0.892 | 0.168* | 0.174* |

lnPOP | 0.221** | 0.141*** | 0.143** |

lnGDS | 0.189*** | 0.308** | 0.290** |

Constant | 2.805* | 4.016*** | 4.136*** |

Countries | 36 | 36 | 36 |

Diagnostic tests | |||

AR(2) [p-value] | 0.208 | ||

Hansen-J test [p-value] | 0.169 | ||

Hausman (Chi^{2}) | 278.11*** | 278.11 | |

Wald test(Chi^{2}) | 1.20E+05** |

***significant at 1%; **significant at 5%; *significant at 10%.

a mean value of 13.456, a minimum value of −125.681 and a maximum value of 83.287.

The results also indicates that using FE, RE and GMM, HEPC is significant. But, since based on the modified Wald test, the residuals are heteroscedastic and hence the results of simple panel data models are unreliable, the GMM results are given consideration in remaining part of this study. According to the findings, GDPPCGR in SSA does not statistically and significantly depend on its lagged value, whose coefficient elasticity is 0.417. This indicates that if GDPPCGR increases by 1 percentage point this year, ceteris paribus, it will not necessarily increase the next year. The coefficient of HEPC is positive and significant but with a weak magnitude (0.157). It implies that a 1% increase in HEPC, other things being constant, affects economic growth by 0.157%. While the coefficients of the control variables FDI, POP and GDS are positive and statistically significant, that of the ODA is negative and statistically insignificant. The magnitude of the coefficients of FDI, POP and GDS are 0.209, 0.221 and 0.189 respectively; suggesting that an increase of 1% in FDI, POP and GDS, ceteris paribus, tend to increase GDPPCGR by 0.209%, 0.221% and 0.189% respectively.

The paper has investigated the relationship between health expenditure and economic growth in Sub-Saharan Africa. The relationship between the two variables is found to be significant but weak in terms of magnitude. An important finding is that official development assistance does not improve economic growth of the region because the effect is not significant. A possible reason attributed to this may be the fact that most of the time donors allocate official development assistance under some conditions which may not be the priorities of the recipient countries. Further, foreign direct investment appears to be a significant determinant of economic growth in Sub-Saharan Africa, thus better policies for the attractiveness of FDI need to be developed and implemented in the region. Our findings show that health care is a necessity rather than a luxury in Sub- Saharan Africa, hence policy makers should consider this aspect as a way offostering economic growth in the region. Finally, another important policy implication of the results is the potential contribution of the young population to the improvement of the economic growth of the region if it is used in an efficient manner.

We are grateful to the NSFC 41272362/41572315 for its support.

Aboubacar, B. and Xu, D.Y. (2017) The Impact of Health Ex- penditure on the Economic Growth in Sub- Saharan Africa. Theoretical Economics Let- ters, 7, 615-622. https://doi.org/10.4236/tel.2017.73046

Benin | South Africa | Kenya | Namibia |
---|---|---|---|

Burkina Faso | Guinea-Bissau | Madagascar | Swaziland |

Central African Republic | Equatorial Guinea | Mauritius | Tanzania |

Cote d'Ivoire | Mali | Rwanda | |

Cameroon | Mauritania | Sudan | |

The Republic of Congo | Niger | Seychelles | |

The Democratic Republic of Congo | Nigeria | Uganda | |

Gabon | Senegal | Angola | |

Ghana | Sierra Leone | Botswana | |

Guinea | Chad | Mozambique | |

Comoros | Togo | Malawi |